Merge pull request #6244 from MervinPraison/claude/issue-6242-20261105

fix: add --no-push / ollama_push: false to keep Ollama exports local

src/praisonai-train/praisonai_train/cli/commands/train.py

@@ -717,6 +717,9 @@ def train_export(
     mtp_draft: bool = typer.Option(
         False, "--mtp-draft/--no-mtp-draft",
         help="Also download the stock MTP drafter (Gemma-4 only) for fast inference"),
+    push: Optional[bool] = typer.Option(
+        None, "--push/--no-push",
+        help="ollama: push to ollama.com after `ollama create` (--no-push keeps it local)"),
 ):
     """
     Export / publish an ALREADY-trained model without re-running training.
@@ -726,6 +729,7 @@ def train_export(
         praisonai-train export gguf   --model-dir lora_model --quant q4_k_m            # local .gguf
         praisonai-train export gguf   --model-dir lora_model --hf   me/my-model        # local + push
         praisonai-train export ollama --model-dir lora_model --ollama me/my-model --quant q4_k_m
+        praisonai-train export ollama --model-dir lora_model --ollama my-model --no-push  # local only
     """
     from ..output.console import get_output_controller
 
@@ -765,6 +769,9 @@ def train_export(
         cfg["hf_model_name"] = hf
     if ollama:
         cfg["ollama_model"] = ollama
+    # Either explicit flag overrides the config; unset keeps config/default (push).
+    if isinstance(push, bool):
+        cfg["ollama_push"] = push
 
     # Recover the model name (drives chat-template selection) from --base-model or
     # the trained model's config.json (_name_or_path), falling back to the dir name.

src/praisonai-train/praisonai_train/setup/config.yaml

@@ -1,4 +1,5 @@
 ollama_save: "true"
+ollama_push: "true"  # set "false" to keep the Ollama model local (no upload to ollama.com)
 huggingface_save: "true"
 train: "true"
 

src/praisonai-train/praisonai_train/train/_ollama.py

@@ -220,8 +220,9 @@ def create_and_push_ollama_model(
     model_parameters: str,
     modelfile_content: str,
     quantization: Optional[str] = None,
+    push: bool = True,
 ) -> None:
-    """Create and push an Ollama model with proper daemon management.
+    """Create (and by default push) an Ollama model with proper daemon management.
 
     Args:
         ollama_model: Name of the Ollama model
@@ -229,6 +230,8 @@ def create_and_push_ollama_model(
         modelfile_content: Content for the Modelfile
         quantization: Optional quantization (e.g. "q4_k_m") passed to
             ``ollama create --quantize`` so the model isn't stored as huge f16.
+        push: When False, stop after a successful ``ollama create`` and keep the
+            model local-only (nothing is uploaded to ollama.com).
 
     Raises:
         RuntimeError: If ollama create/push operations fail (with actionable guidance)
@@ -259,6 +262,10 @@ def create_and_push_ollama_model(
             f"{(create.stderr or create.stdout or '').strip()}"
         )
 
+    if not push:
+        print(f"Created local Ollama model `{tag}` (not pushed). Run it with: ollama run {tag}")
+        return
+
     # Push with captured output so a 401/403 becomes an actionable message rather
     # than a raw non-zero exit.
     result = subprocess.run(["ollama", "push", tag], capture_output=True, text=True)

src/praisonai-train/praisonai_train/train/llm/trainer.py

@@ -545,6 +545,7 @@ class TrainModel:
         "optim", "weight_decay", "lr_scheduler_type", "seed", "output_dir",
         "assistant_only_loss", "train_on_responses_only", "save_steps",
         "train", "huggingface_save", "huggingface_save_gguf", "ollama_save",
+        "ollama_push",
         "method", "beta", "max_prompt_length", "desirable_weight", "undesirable_weight",
         "reward_funcs", "num_generations", "max_completion_length",
         "embedding_learning_rate",
@@ -1784,6 +1785,7 @@ class TrainModel:
             self.config.get('model_parameters', 'latest'),
             modelfile_content,
             quantization=self.config.get('quantization_method'),
+            push=self._flag(self.config.get('ollama_push'), default=True),
         )
 
     def run(self):

src/praisonai-train/praisonai_train/train_vision.py

@@ -313,7 +313,8 @@ PARAMETER top_p 0.9
         create_and_push_ollama_model(
             self.config['ollama_model'], 
             self.config['model_parameters'], 
-            modelfile_content
+            modelfile_content,
+            push=self._flag(self.config.get('ollama_push'), default=True),
         )
 
     def run(self):

src/praisonai-train/tests/unit/train/test_robustness.py

@@ -412,3 +412,88 @@ def test_export_command_bad_target_exits(monkeypatch, tmp_path):
             quant=None,
             base_model=None,
         )
+
+
+def test_ollama_no_push_skips_push(monkeypatch, tmp_path):
+    """push=False stops after `ollama create`; nothing is sent to ollama.com."""
+    monkeypatch.chdir(tmp_path)
+    monkeypatch.setattr(ollama_mod, "_check_ollama_disk", lambda *_a, **_k: None)
+    monkeypatch.setattr(ollama_mod, "ensure_ollama_running", lambda *_a, **_k: None)
+    calls = []
+
+    def _fake_run(cmd, *a, **k):
+        calls.append(cmd)
+        return types.SimpleNamespace(returncode=0, stdout="", stderr="")
+
+    monkeypatch.setattr(ollama_mod.subprocess, "run", _fake_run)
+    ollama_mod.create_and_push_ollama_model(
+        "my-model", "latest", "FROM ./model\n", push=False)
+    assert [c[1] for c in calls] == ["create"]
+    assert not any("push" in c for c in calls)
+
+
+def _ollama_export_cfg(monkeypatch, tmp_path, push, config_push=None):
+    """Run `export ollama` with a fake trainer; return the config it received."""
+    import praisonai_train.cli.commands.train as cli
+
+    seen = {}
+
+    class _FakeTrainer:
+        @classmethod
+        def for_export(cls, cfg):
+            obj = cls()
+            obj.config = cfg
+            obj.model = None
+            obj.hf_tokenizer = None
+            return obj
+
+        def load_model(self):
+            return ("MODEL", "TOK")
+
+        def create_and_push_ollama_model(self):
+            seen.update(self.config)
+
+    monkeypatch.setattr(trainer_mod, "TrainModel", _FakeTrainer)
+    config = None
+    if config_push is not None:
+        config = tmp_path / "cfg.yaml"
+        config.write_text(f"ollama_push: {config_push}\n")
+    cli.train_export(
+        target="ollama", model_dir=str(tmp_path), config=str(config) if config else None,
+        ollama="my-model", hf=None, quant=None, base_model="unsloth/gemma-2-2b",
+        mtp_draft=False, push=push,
+    )
+    return seen
+
+
+def test_export_ollama_no_push_flag_sets_config(monkeypatch, tmp_path):
+    assert _ollama_export_cfg(monkeypatch, tmp_path, push=False)["ollama_push"] is False
+
+
+def test_export_ollama_explicit_push_overrides_config(monkeypatch, tmp_path):
+    cfg = _ollama_export_cfg(monkeypatch, tmp_path, push=True, config_push="false")
+    assert cfg["ollama_push"] is True
+
+
+def test_export_ollama_unset_push_keeps_config(monkeypatch, tmp_path):
+    cfg = _ollama_export_cfg(monkeypatch, tmp_path, push=None, config_push="false")
+    assert cfg["ollama_push"] is False  # YAML bool preserved, not overridden
+    assert "ollama_push" not in _ollama_export_cfg(monkeypatch, tmp_path, push=None)
+
+
+@pytest.mark.parametrize("value, expected", [
+    (False, False), ("false", False), (True, True), ("true", True), (None, True),
+])
+def test_llm_trainer_forwards_ollama_push(monkeypatch, value, expected):
+    """TrainModel forwards `ollama_push` (bool or string) to the helper."""
+    captured = {}
+    monkeypatch.setattr(
+        ollama_mod, "create_and_push_ollama_model",
+        lambda *a, **k: captured.update(k))
+    fake = types.SimpleNamespace(
+        config={"ollama_model": "m", "ollama_push": value},
+        prepare_modelfile_content=lambda: "FROM ./model\n",
+        _flag=trainer_mod.TrainModel._flag,
+    )
+    trainer_mod.TrainModel.create_and_push_ollama_model(fake)
+    assert captured["push"] is expected